Alaka Shakiru A, Ngan So-Fong Cam, Shookoni Mostafa, MacPherson Rebecca Ek, Faught Brent E, Klentrou Panagiota, Kalaria Raj, Chen Christopher P, Sze Siu Kwan
Department of Health Science, Faculty of Applied Health Sciences, Brock University, St. Catharines, Ontario, Canada.
Centre for Neuroscience, Brock University, St. Catharines, Ontario, Canada.
Alzheimers Dement. 2025 Aug;21(8):e70442. doi: 10.1002/alz.70442.
The clinical utility of dementia prognostic scores has limited validity across diverse populations. This study aimed to enhance the Cardiovascular Risk Factors, Aging and Dementia (CAIDE) model by incorporating resting heart rate (RHR) using a machine learning method across a diverse population.
We developed CAIDE and CAIDE-RHR models using a random forest algorithm in the National Alzheimer's Coordinating Center (NACC) dataset. Model performances were assessed using area under the receiver-operating characteristic curve (AUC), Matthew's correlation coefficient (MCC), and the Brier score.
Incorporating RHR into the CAIDE model significantly improved predictive accuracy across Black African, Asian, White, and Native Hawaiian populations (mean AUC range: 0.80-0.91). However, this improvement was not observed in the American Indian population, where the AUC decreased from 0.87 to 0.84.
Our findings highlight significant ethnic differences in dementia risk prediction models. These results underscore the need for validating and tailoring dementia risk scores to ensure applicability across diverse races.
Incorporating resting heart rate (RHR) into the Cardiovascular Risk Factors, Aging, and Dementia (CAIDE) model significantly improves its predictive accuracy for dementia risk across diverse populations, offering a novel addition to dementia risk models. The application of the machine learning technique enhances dementia risk prediction by capturing complex, non-linear relationships among variables. The improved model enables more precise early identification of individuals at risk of cognitive decline, supporting preventive strategies in dementia care. Resting heart rate, a simple and non-invasive cardiovascular measure, is demonstrated to be a valuable predictor for dementia risk, making it practical for clinical application.
痴呆症预后评分在不同人群中的临床效用有效性有限。本研究旨在通过机器学习方法将静息心率（RHR）纳入心血管危险因素、衰老与痴呆（CAIDE）模型，以改进该模型，研究对象为不同人群。
我们在国家阿尔茨海默病协调中心（NACC）数据集中使用随机森林算法开发了CAIDE模型和CAIDE-RHR模型。使用受试者工作特征曲线下面积（AUC）、马修斯相关系数（MCC）和布里尔评分评估模型性能。
将RHR纳入CAIDE模型显著提高了在非洲黑人、亚洲人、白人及夏威夷原住民人群中的预测准确性（平均AUC范围：0.80 - 0.91）。然而，在美洲印第安人群中未观察到这种改善，该人群的AUC从0.87降至0.84。
我们的研究结果突出了痴呆症风险预测模型中显著的种族差异。这些结果强调了验证和调整痴呆症风险评分以确保在不同种族中适用性的必要性。
将静息心率（RHR）纳入心血管危险因素、衰老与痴呆（CAIDE）模型显著提高了其在不同人群中对痴呆症风险的预测准确性，为痴呆症风险模型增添了新内容。机器学习技术的应用通过捕捉变量之间复杂的非线性关系增强了痴呆症风险预测。改进后的模型能够更精确地早期识别有认知衰退风险的个体，支持痴呆症护理中的预防策略。静息心率作为一种简单且非侵入性的心血管测量指标，被证明是痴呆症风险的重要预测指标，具有临床应用价值。
